An optimal integrated QSMS model from cluster analysis

Odigie, Michael E.; Badar, M. Affan; Sinn, John W.; Moayed, Farman A.; Shahhosseini, Mehran Ali

doi:10.1108/tqm-12-2015-0150

Cited by 9 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cluster analysis extensively finds application in addressing variety of quality related problems. To name few, Odigie et al. , (2017) used cluster analysis to visualize the existing models of quality and safety management systems in the literature and to derive the elements of an integrated quality and safety management system.…”

Section: Discussionmentioning

confidence: 99%

“…Cluster analysis extensively finds application in addressing variety of quality related problems. To name few, Odigie et al, (2017) used cluster analysis to visualize the existing models of quality and safety management systems in the literature and to derive the elements of an integrated quality and safety management system. Jaypuria et al, (2023) used k-means clustering to cluster the finished items into quality grades to further address them with gradespecific course of actions.…”

Section: Cluster Analysismentioning

confidence: 99%

See 1 more Smart Citation

Analytical modeling of quality parameters in casting process – learning-based approach

Suthar

Persis

Gupta

2022

IJQRM

View full text Add to dashboard Cite

PurposeFoundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.Design/methodology/approachThe literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.FindingsThe authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.Originality/valueThis study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Cluster Analysismentioning

confidence: 99%

Analytical modeling of quality parameters in casting process – learning-based approach

Suthar

Persis

Gupta

2022

IJQRM

View full text Add to dashboard Cite

show abstract

“…Besides, according to Arunajadai et al (2004), the ability to detect potential failure modes depends on the experience and knowledge of a designer, so there is a risk that in this phase, there is a risk of failure that may not be identified. Although, in general, failure modes are usually associated with products, according to Odigie et al (2017), failure mode can also be analogous to failure in providing safety in working conditions.…”

Section: Failure Identificationmentioning

confidence: 99%

Complaint management model of manufacturing products using text mining and potential failure identification

Astanti

Sutanto

2021

TQM

View full text Add to dashboard Cite

PurposeThis paper aims to propose a framework on complaint management system for quality management by applying the text mining method and potential failure identification that can support organization learning (OL). Customer complaints in the form of email text is the input of the framework, while the most frequent complaints are visualized using a Pareto diagram. The company can learn from this Pareto diagram and take action to improve their process.Design/methodology/approachThe first main part of the framework is creating a defect database from potential failure identification, which is the initial part of the failure mode and effect analysis technique. The second main part is the text mining of customer email complaints. The last part of the framework is matching the result of text mining with the defect database and presenting in the form of a Pareto diagram. After the framework is proposed, a case study is conducted to illustrate the applicability of the proposed method.FindingsBy using the defect database, the framework can interpret the customer email complaints into the list of most defect complained by customer using a Pareto diagram. The results of the Pareto diagram, based on the results of text mining of consumer complaints via email, can be used by a company to learn from complaint and to analyze the potential failure mode. This analysis helps company to take anticipatory action for avoiding potential failure mode happening in the future.Originality/valueThe framework on complaint management system for quality management by applying the text mining method and potential failure identification is proposed for the first time in this paper.

show abstract

“…Let's return to the discussion of the compatibility of lean and stakeholder theory. Camilleri (2009) in winery, Islam and Tura (2013) in software development, Odigie et al (2017) in safety management, Sisto (2010) in aerospace, Susniene and Vangas (2005) in government, and Smith and Synowka (2014) in purchasing all make the case that lean and stakeholder theory can be integrated. For Bettiol (2013) and Carter (2015), there is little attempt to understand lean thinking's impact on stakeholders.…”

Section: Literature Reviewmentioning

confidence: 99%

A study of the balancing of lean thinking and stakeholder salience in decision-making

Bader

Badar

Rodchua

et al. 2020

TQM

Self Cite

View full text Add to dashboard Cite

PurposeThis research brings together two streams of thought applied to decision-making: lean thinking and stakeholder theory. Both have been identified as ways to improve organizational value. Previous studies disagree regarding whether they can work together. This study investigates if managers balance stakeholders and lean thinking in decision-making.Design/methodology/approachThis research investigates if both lean thinking and stakeholder salience share common literature by using data mining. It surveys organizations that perceive themselves as lean and have multiple diverse stakeholders to determine whether waste and salience are considered when making decisions. An ANOVA is done to see if organization type, management level, organization size, geographic location, or lean maturity has an effect on the priority of stakeholder salience or lean thinking's waste variants when making decisions.FindingsFindings of this research are: 1) stakeholders salience criteria are considered more often than lean thinking's waste variants in decision-making by managers as a whole and in particular by middle-level managers and senior managers. However, lean thinking's waste variants are considered as often as stakeholder salience criteria by first-line managers. 2) The ranking of stakeholder salience in making decisions is not affected by organization type, respondent position, organization size, perceived lean experience, or geographic location. The organization type, organization size, lean experience, and location do not affect the ranking of lean thinking variants either. But the ranking of lean thinking's waste variants is significantly different for first-line, middle-level, and senior managers. Middle-level managers rank lean thinking higher than that of either first-line or senior-level. Because of this, middle managers have a more balanced approach in using lean thinking and stakeholder salience than other managers. 3) Stakeholder salience criteria have a significantly higher ranking than lean thinking variants in making decisions for all organization types: manufacturing and nonmanufacturing.Originality/ValueThis research demonstrates a significant disconnect exists between lean thinking and demands of stakeholders that impacts the value of an organization, and only middle-level managers bring balance and awareness of both streams of thought. An empirical instrument has been developed to balance the stakeholder salience criteria with the lean thinking variants.

show abstract

An optimal integrated QSMS model from cluster analysis

Cited by 9 publications

References 43 publications

Analytical modeling of quality parameters in casting process – learning-based approach

Analytical modeling of quality parameters in casting process – learning-based approach

Complaint management model of manufacturing products using text mining and potential failure identification

A study of the balancing of lean thinking and stakeholder salience in decision-making

Contact Info

Product

Resources

About